The invention is generally directed to an integrated system of blood glucose level detection and use of that information in setting insulin delivery parameters, and more particularly, to the use of actual sensor data in characterizing a sensor for use in performing preclinical closed-loop trial studies in silico.
Diabetes is a metabolic disorder that afflicts tens of millions of people throughout the world. Diabetes results from the inability of the body to properly utilize and metabolize carbohydrates, particularly glucose. Normally, the finely-tuned balance between glucose in the blood and glucose in bodily tissue cells is maintained by insulin, a hormone produced by the pancreas which controls, among other things, the transfer of glucose from blood into body tissue cells. Upsetting this balance causes many complications and pathologies including heart disease, coronary and peripheral artery sclerosis, peripheral neuropathies, retinal damage, cataracts, hypertension, coma, and death from hypoglycemic shock.
In patients with insulin-dependent diabetes, the symptoms of the disease can be controlled by administering additional insulin (or other agents that have similar effects) by injection or by external or implantable insulin pumps. The “correct” insulin dosage is a function of the level of glucose in the blood. Ideally, insulin administration should be continuously readjusted in response to changes in blood glucose level. In diabetes management, “insulin” instructs the body's cells to take in glucose from the blood. “Glucagon” acts opposite to insulin, and causes the liver to release glucose into the blood stream. The “basal rate” is the rate of continuous supply of insulin provided by an insulin delivery device (pump). The “bolus” is the specific amount of insulin that is given to raise blood concentration of the insulin to an effective level when needed (as opposed to continuous).
Presently, systems are available for continuously monitoring blood glucose levels by implanting a glucose sensitive probe into the patient. Such probes measure various properties of blood or other tissues, including optical absorption, electrochemical potential, and enzymatic products. The output of such sensors can be communicated to a hand held device that is used to calculate an appropriate dosage of insulin to be delivered into the blood stream in view of several factors, such as a patient's present glucose level, insulin usage rate, carbohydrates consumed or to be consumed, and exercise, among others. These calculations can then be used to control a pump that delivers the insulin, either at a controlled basal rate, or as a bolus. When provided as an integrated system, the continuous glucose monitor, controller, and pump work together to provide continuous glucose monitoring and insulin pump control.
Such systems at present require intervention by a patient to calculate and control the amount of insulin to be delivered. However, there may be periods when the patient is not able to adjust insulin delivery. For example, when the patient is sleeping, he or she cannot intervene in the delivery of insulin, yet control of a patient's glucose level is still necessary. A system capable of integrating and automating the functions of glucose monitoring and controlled insulin delivery would be useful in assisting patients in maintaining their glucose levels, especially during periods of the day when they are unable to intervene.
Since the year 2000, at least five continuous or semi-continuous glucose monitors have received regulatory approval.1 In combination with continuous subcutaneous insulin infusion (“CSII”),2 these devices have promoted research toward closed-loop systems, which deliver insulin according to real-time needs, as opposed to open-loop systems which lack the real-time responsiveness to changing glucose levels. A closed-loop system, also called the “artificial pancreas,” consists of three components: a glucose monitoring device such as a continuous glucose monitor (“CGM”) that measures subcutaneous glucose concentration (“SC”); a titrating algorithm to compute the amount of analyte such as insulin and/or glucagon to be delivered; and one or more analyte pumps to deliver computed analyte doses subcutaneously. So far, only a few prototypes have been developed, and testing has been confined to clinical settings.3-8 However, an aggressive concerted effort promises accelerated progress toward home testing of closed-loop systems.
The development, evaluation, and testing of closed-loop systems are time-consuming, costly, and confounded by ethical and regulatory issues. Apart from early stage testing in animals such as the dog9,10 or the swine,11 testing in the computer (virtual) environment, also termed in silico testing, is the only other alternative to evaluate and optimize control algorithms outside human studies. Chassin and colleagues have developed a simulation environment and testing methodologyl2 using a glucoregulatory model developed in a multitracer study13 and evaluated a glucose controller developed within the Adicol Project.14 Another simulator has been reported by Cobelli and associates,15 building on model-independent quantification of glucose fluxes occurring during a meal.16 The latter simulator has been accepted by the U.S. Food and Drug Administration to replace animal testing. Patek and coworkers provided guidelines for preclinical testing of control algorithms.17 
However, such simulations have used mathematical models of glucose sensors in which random data is used for simulating errors of the sensor. Random number generators are used to simulate random errors of such sensors based on noise of the sensor. Such data are therefore not based on the actual performance of any particular sensor and are likely to have a significant level of inaccuracy.
Closed-loop systems may revolutionize management of type 1 diabetes mellitus (“T1DM”), but their introduction is likely to be gradual, starting from simpler applications such as hypoglycemia prevention or overnight glucose control and progressing to more complex approaches such as twenty-four hours per day/seven days per week (24/7) glucose control.8 The main reason for gradual deployment is the uncertain risk of hypoglycemia and hyperglycemia, which may arise due to (1) intrinsic overdosing and underdosing of insulin by a control algorithm, and (2) persistent and transient differences between plasma glucose (“PG”) and sensor glucose (“SG”). The transient differences could be either of physiological origin (SC glucose kinetics) or due to a temporal CGM device artifact. The persistent differences result from the CGM calibration error (“CE”). The relatively slow absorption of subcutaneously administered “rapid-acting” insulin analogues and other system imperfections such as pump delivery errors may exacerbate the hypoglycemia and hyperglycemia risks.
Hence, those of skill in the art have recognized a need for an integrated, automated system combining continuous glucose monitoring and controlled insulin delivery. Such a system would include various features to insure the accuracy of the glucose monitor and to protect the user from either under- or over-dosage of insulin. The system would include various functions for improving the accuracy, usability, control, and safety of the system, including a variety of alarms which could be set by a user or a technician to avoid false alarms while ensuring adequate sensitivity to protect the user. Those skilled in the art have also recognized a need for a more accurate glucose measurement error model for increasing the accuracy of closed-loop systems. The present invention fulfills these, and other needs.